Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, Methodology.

Cover -- Title Page -- Copyright -- Contributors -- Contents -- Preface -- Chapter 1: Computational Modeling in Cognition and Cognitive Neuroscience -- Mathematical Models as Cognitive Prosthesis -- Models of Choice Reaction Time Tasks -- Models of Rehearsal in Short-Term Memory -- The Need for Cognitive Prostheses -- Classes of Models -- Descriptive Models -- Theoretical Models -- Measurement Models -- Translating Data Into Parameters -- Summary -- Explanatory Models -- Explaining Scale Invariance in Memory -- Explanatory Necessity Versus Sufficiency -- Model Selection and Model Complexity -- Quantitative Fit and Qualitative Predictions -- Summary -- Cognitive Architectures -- Production Systems: ACT-R -- Neural-Network Architectures: Spaun -- Relating Architectures to Data -- The Use of Models in Cognitive Neuroscience -- Conclusion -- References -- Chapter 2: Bayesian Methods in Cognitive Modeling -- Introduction -- Advantages of Bayesian Methods -- Overview -- A Case Study -- Experimental Data -- Research Questions -- Model Development -- Graphical Model Representation -- Prior Prediction -- Alternative Models With Vague Priors -- Parameter Inference -- Posterior Prediction -- Interpreting and Summarizing the Posterior Distribution -- Model Testing Using Prior and Posterior Distributions -- Sensitivity Analysis -- Latent-Mixture Modeling -- Hierarchical Modeling -- Finding Invariances -- Common-Cause Modeling -- Prediction and Generalization -- Conclusion -- References -- Chapter 3: Model Comparison in Psychology -- Introduction -- Foundations of Model Comparison -- Model Evaluation Criteria -- Follies of a Good Fit -- Generalizability: The Yardstick of Model Comparison -- The Importance of Model Complexity -- The Practice of Model Comparison -- Model Falsifiability, Identifiability, and Equivalence -- Model Estimation.

Methods of Model Comparison -- Illustrated Example -- Conclusion -- Appendix A - Matlab Code for Illustrated Example -- Appendix B - R2JAGS Code for Illustrated Example -- References -- Chapter 4: Statistical Inference -- What Is Statistical Inference? -- Populations and Parameters -- Frequentist Approaches -- Point Estimation -- Hypothesis Testing -- Relevance of Stopping Rules -- The Likelihood Approach -- Parameter Estimation -- Using Likelihood for Frequentist Inference -- The Likelihood Principle -- Bayesian Approaches -- From Prior to Posterior -- Informing the Choice of Prior -- Parameter Estimation -- Hypothesis Testing -- Broader Considerations -- Parametric and Nonparametric Inference -- Model Checking -- Conclusion -- References -- Chapter 5: Elementary Signal Detection and Threshold Theory -- Thurstone's Law of Comparative Judgment -- SDT and the Introduction of a Decision Stage -- Receiver Operating Characteristic Functions -- Beyond the EVSDT -- The Confidence-Rating Method -- Characterizing Performance Across Conditions -- Forced Choice, Ranking Judgments, and the Area Theorem -- Multidimensional SDT -- Threshold Theory -- A Note on Data Aggregation -- Conclusion -- References -- Chapter 6: Cultural Consensus Theory -- Introduction -- An Example of a CCT Analysis of Response Profile Data -- A CCT Analysis of the Mathematics Course Exam Responses -- The General Condorcet Model -- Axioms for the GCM -- Properties of the GCM -- Some Empirical Studies Using the GCM -- The Multiculture GCM -- CCT Models Where Consensus Truth Is on a Continuum -- CCT Models for Continuous Responses -- A CCT Model for an Ordinal (Likert) Scale -- CCT Models for Other Questionnaire Designs -- Allowing a "Don't-Know" Response in the GCM -- CCT Models for Determining Consensus Ties in a Network -- CCT Models for Ranking and Matching Responses.

Statistical Inference for CCT Models -- Bayesian Statistical Inference -- Incorporating Covariates in Estimation -- Bayesian Model Checks -- Software and Graphic User Interfaces (GUIs) for CCT Models -- Nonhierarchical Bayesian Software for Fitting CCT Models -- Hierarchical Bayesian Software Packages -- Hierarchical Condorcet Modeling Toolbox -- Hierarchical, Extended Condorcet Model to Capture Uncertainty in Decision Making -- CCTpack-An R Package for Hierarchical Bayesian Implementations of Single and Multicultural Versions of CCT Models for Binary, Ordered Categorical, and Continuous Data -- Helpful Commands -- Alternative Methods for Applying CCT Methodology and Models -- Conclusion -- Appendix: Proofs of Observations -- References -- Chapter 7: Methods in Psychophysics -- Introduction -- Scope -- Structure -- Some Examples -- Contrast Sensitivity Functions -- Visual-Haptic Integration -- Context Effects in Brightness Perception -- Gender Classification -- Psychometric Functions -- Data Collection -- Setting Up the Hardware -- Experimental Tasks -- Relating Different Tasks -- Experimental Design -- Best Practice -- Data Analysis -- The Psychometric Function I: The Binomial Model -- The Psychometric Function II: The Binomial Mixture Model -- Violations of the i.i.d. Assumption -- The Psychometric Function III: The Beta-Binomial Mixture Model -- The Psychometric Function IV: Bayesian Inference -- Importance of the Width of the Psychometric Function -- Bias and Sensitivity Differences in 2IFC and 2AFC -- Multidimensional Psychometric Functions -- Conclusion -- References -- Chapter 8: The Categorization Experiment: Experimental Design and Data Analysis -- Introduction -- Categorization versus Identification -- Category Structure -- Rule-Based Category-Learning Tasks -- Information-Integration Category-Learning Tasks.

Unstructured Category-Learning Tasks -- Prototype-Distortion Category-Learning Tasks -- Stimulus Choices -- Real-World Versus Artificial Stimuli -- Binary- Versus Continuous-Valued Stimulus Dimensions -- Separable Versus Integral Dimensions -- Number of Stimulus Dimensions -- Constructing the Categories -- RB and II Categories: The Randomization Technique -- Prototype-Distortion Categories -- Feedback Choices -- Supervised Versus Unsupervised Training -- Observational Versus Feedback-Based Training -- Feedback Timing -- Deterministic Versus Probabilistic Feedback -- Assessing Performance -- Data Analysis -- Forward- Versus Backward-Learning Curves -- Decision-Bound Modeling -- Explicit-Rule Models -- Procedural-Learning Models -- Guessing Models -- Model Fitting -- Conclusion -- List of Abbreviations -- References -- Chapter 9: Response Times and Decision-Making -- Introduction -- Overview of Decision-Making Models -- Interim Summary -- Response Time Models as Theory Development -- Speed-Accuracy Tradeoff -- Fast and Slow Errors -- Choices Between More Than Two Options -- Nonstationary Decision Processes -- Response Times in Cognitive Science and Neuroscience -- Examples of Cognitive Neuroscience Linked with RT Models -- Response Time Models as Measurement Tools -- Parameter Estimation -- Model Selection -- Model Fit -- Conclusion -- References -- Chapter 10: The Stop-Signal Paradigm -- Introduction -- Independent Horse-Race Model of Response Inhibition -- Early Horse-Race Models -- Independent Horse-Race Model: The Basics -- Independent Horse-Race Model With Constant SSRT -- The Complete Independent Horse-Race Model -- Independence Assumptions -- Stop-Signal Reaction Times -- Estimating Summary Measures of SSRT -- Estimating SSRT Variability -- Estimating SSRT Distributions -- Process Models of Response Inhibition.

Describing the Properties of the Go and Stop Process -- Describing How Responses Are Inhibited -- Testing the Goodness-of-Fit of the Horse-Race Model -- Nonparametric Methods -- Parametric Methods -- The Independence Assumption in Practice -- Variants of the Stop-Signal Task -- Stopping in Stop-Change and Selective Stop Tasks -- Discrete Versus Continuous Tasks -- Users' Guidelines -- How to Run Stop-Signal Experiments -- How to Report Stop-Signal Experiments -- How to Interpret Stop-Signal Data -- Conclusion -- List of Abbreviations -- References -- Chapter 11: Uncovering Mental Architecture and Related Mechanisms in Elementary Human Perception, Cognition, and Action -- Introduction: Brief History and General Conception -- Major Characteristics of Elementary Cognitive Systems and Theorems of Mimicry: The Event Space Basis -- Strong Experimental Tests on Response Times I: Event Space Expansion -- Strong Experimental Tests on Response Times II: Applications of Selective Influence -- Strong Experimental Tests on Response Frequencies -- Back to the State Spaces: Tests Based on Partial States of Completion -- Manipulating Process Durations of Available Information -- Conclusion -- References -- Appendix: Applications of Systems Factorial Technology -- Age-Related Changes in Perception and Cognition -- Binocular Interaction -- Categorization -- Cognitive Control -- Human-Machine Teaming -- Identity and Emotion Perception -- Individual Differences/Clinical Populations -- Learning and Reward Processing -- Memory Search -- Multimodal Interaction -- Perceptual Organization -- Perceptual Detection -- Temporal Order Processing -- Visual Search/Visual Attention -- Working Memory/Cognitive Load -- Chapter 12: Convergent Methods in Memory Research -- Introduction: Background on Convergent Methods/Analyses.

An Empirical and Simulation Test of Vertical Convergence in Decision Models.

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